• Title, Summary, Keyword: Histogram-based Classification

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Content-based image retrieval using adaptive representative color histogram and directional pattern histogram (적응적 대표 컬러 히스토그램과 방향성 패턴 히스토그램을 이용한 내용 기반 영상 검색)

  • Kim Tae-Su;Kim Seung-Jin;Lee Kuhn-Il
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.42 no.4
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    • pp.119-126
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    • 2005
  • We propose a new content-based image retrieval using a representative color histogram and directional pattern histogram that is adaptive to the classification characteristics of the image blocks. In the proposed method the color and pattern feature vectors are extracted according to the characteristics o: the block classification after dividing the image into blocks with a fixed size. First, the divided blocks are classified as either luminance or color blocks depending on the saturation of the block. Thereafter, the color feature vectors are extracted by calculating histograms of the block average luminance co-occurrence for the luminance block and the block average colors for the color blocks. In addition, block directional pattern feature vectors are extracted by calculating histograms after performing the directional gradient classification of the luminance. Experimental results show that the proposed method can outperform the conventional methods as regards the precision and the size of the feature vector dimension.

Two-wheelers Detection using Local Cell Histogram Shift and Correlation (국부적 Cell 히스토그램 시프트와 상관관계를 이용한 이륜차 인식)

  • Lee, Sanghun;Lee, Yeunghak;Kim, Taesun;Shim, Jaechang
    • Journal of Korea Multimedia Society
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    • v.17 no.12
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    • pp.1418-1429
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    • 2014
  • In this paper we suggest a new two-wheelers detection algorithm using local cell features. The first, we propose new feature vector matrix extraction algorithm using the correlation two cells based on local cell histogram and shifting from the result of histogram of oriented gradients(HOG). The second, we applied new weighting values which are calculated by the modified histogram intersection showing the similarity of two cells. This paper applied the Adaboost algorithm to make a strong classification from weak classification. In this experiment, we can get the result that the detection rate of the proposed method is higher than that of the traditional method.

An Object Classification Algorithm Based on Histogram of Oriented Gradients and Multiclass AdaBoost

  • Yun, Anastasiya;Lenskiy, Artem;Lee, Jong Soo
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.1 no.3
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    • pp.83-89
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    • 2008
  • This paper introduces a visual object classification algorithm based on statistical information. Objects are characterized through the Histogram of Oriented Gradients (HOG) method and classification is performed using Multiclass AdaBoost. Salient features of an object's appearance are detected by HOG blocks Blocks of different sizes are tested to define the most suitable configuration. To select the most informative blocks for classification a multiclass AdaBoostSVM algorithm is applied. The proposed method has a high speed processing and classification rate. Results of the evaluation based on example of hand gesture recognition are presented.

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TFT-LCD Defect Detection based on Histogram Distribution Modeling (히스토그램 분포 모델링 기반 TFT-LCD 결함 검출)

  • Gu, Eunhye;Park, Kil-Houm;Lee, Jong-Hak;Ryu, Gang-Soo;Kim, Jungjoon
    • Journal of Korea Multimedia Society
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    • v.18 no.12
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    • pp.1519-1527
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    • 2015
  • TFT-LCD automatic defect inspection system for detecting defects in place of the visual tester does pre-processing, candidate defect pixel detection, and recognition and classification through a blob analysis. An over-detection result of defects acts as an undue burden of blob analysis for recognition and classification. In this paper, we propose defect detection method based on the histogram distribution modeling of TFT-LCD image to minimize over-detection of candidate defective pixels. Primary defect candidate pixels are detected estimating the skewness of the luminance distribution histogram of the background pixels. Based on the detected defect pixels, the defective pixels other than noise pixels are detected using the distribution histogram model of the local area. Experimental results confirm that the proposed method shows an excellent defect detection result on the image containing the various types of defects and the reduction of the degree of over-detection as well.

New Approach to Two-wheeler Detection using Correlation Coefficient based on Histogram of Oriented Gradients

  • Lee, Yeunghak;Shim, Jaechang
    • Journal of Multimedia Information System
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    • v.3 no.4
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    • pp.119-128
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    • 2016
  • This study aims to suggest a new algorithm for detecting two-wheelers on road that have various shapes according to the viewing angle for vision based intelligent vehicles. This article describes a new approach to two-wheelers detection algorithm riding on people based on modified Histogram of Oriented Gradients (HOG) using correlation coefficient (CC). The CC between two local area variables, in which one is the person riding a bike and other is its background, can represent correlation relation. First, we extract edge vectors using HOG which includes gradient information and differential magnitude as cell based. And then, the value, which is calculated by the CC between the area of each cell and one of two-wheelers, can be extracted as the weighting factor in process for normalizing the modified HOG cell. This paper applied the Adaboost algorithm to make a strong classification from weak classification. In this experiment, we can get the result that the detection rate of the proposed method is higher than that of the traditional method.

Two-wheeler Detection System using Histogram of Oriented Gradients based on Local Correlation Coefficients and Curvature

  • Lee, Yeunghak;Kim, Taesun;Shim, Jaechang
    • Journal of Multimedia Information System
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    • v.2 no.4
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    • pp.303-310
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    • 2015
  • Vulnerable road users such as bike, motorcycle, small automobiles, and etc. are easily attacked or threatened with bigger vehicles than them. So this paper suggests a new approach two-wheelers detection system riding on people based on modified histogram of oriented gradients (HOGs) which is weighted by curvature and local correlation coefficient. This correlation coefficient between two variables, in which one is the person riding a bike and other is its background, can represent correlation relation. First, we extract edge vectors using the curvature of Gaussian and Histogram of Oriented Gradients (HOG) which includes gradient information and differential magnitude as cell based. And then, the value, which is calculated by the correlation coefficient between the area of each cell and one of bike, can be used as the weighting factor in process for normalizing the HOG cell. This paper applied the Adaboost algorithm to make a strong classification from weak classification. The experimental results validate the effectiveness of our proposed algorithm show higher than that of the traditional method and under challenging, such as various two-wheeler postures, complex background, and even conclusion.

Comparative Assessment of Fractal Analysis and Histogram in Canine Abdominal Ultrasonographic Images (개 복부초음파영상의 프랙탈 분석과 히스토그램 분석의 비교평가)

  • Choi, Ho-Jung;Lee, Young-Won;Jung, In-Jo;Wang, Ji-Hwan;Lee, Kyung-Woo;Yeon, Seong-Chan;Lee, Hyo-Jong;Lee, Hee-Chun
    • Journal of Veterinary Clinics
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    • v.24 no.4
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    • pp.568-572
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    • 2007
  • This study was carried out to show at the fractal analysis complements the practical disadvantage of gray level histogram which is designed to measure the quantitative classification of echo patterns in ultrasonographic image of parenchymal organs such as spleen and kidney and it is a practical method of measurement for quantitative classification. By using ultrasonographs, kidney and spleen of 21 healthy Beagles were fixed under different gain settings to be scanned for echo patterns and results were analyzed with body gray level histogram and fractal analysis. Then it was compared based on the statistical data obtained. Although there was a proportionate increase in histogram along with gain settings, there were consistencies in the fractal dimension. In terms of quantitative analysis in ultrasonographic images, fractal analysis is concluded to complement the practical disadvantage of gray level histogram.

Natural Image Labeling and Classification Technique by Color-Spatial Histogram and Production Rules (칼라-공간 히스토그램과 생성 규칙을 이용한 자연 영상 레이블링 및 분류 기법)

  • 김준영;신수연;김우생
    • Proceedings of the IEEK Conference
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    • pp.153-156
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    • 2002
  • The image labeling and classification is one of the important tasks for a content-based image retrieval and an image understanding. This paper propose a new technique to label and classify natural images with a color-spatial histogram and production rules. We show that our proposed method is very efficient for a natural image composed of a few regions.

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A Natural Scene Statistics Based Publication Classification Algorithm Using Support Vector Machine (서포트 벡터 머신을 이용한 자연 연상 통계 기반 저작물 식별 알고리즘)

  • Song, Hyewon;Kim, Doyoung;Lee, Sanghoon
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.42 no.5
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    • pp.959-966
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    • 2017
  • Currently, the market of digital contents such as e-books, cartoons and webtoons is growing up, but the copyrights infringement are serious issue due to their distribution through illegal ways. However, the technologies for copyright protection are not developed enough. Therefore, in this paper, we propose the NSS-based publication classification method for copyright protection. Using histogram calculated by NSS, we propose classification method for digital contents using SVM. The proposed algorithm will be useful for copyright protection because it lets us distinguish illegal distributed digital contents more easily.

Image Enhancement Using Adaptive Region-based Histogram Equalization for Multiple Color-Filter Aperture System (다중 컬러필터 조리개 시스템을 위한 적응적 히스토그램 평활화를 이용한 영상 개선)

  • Lee, Eun-Sung;Kang, Won-Seok;Kim, Sang-Jin;Paik, Joon-Ki
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.48 no.2
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    • pp.65-73
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    • 2011
  • In this paper, we present a novel digital multifocusing approach using adaptive region-based histogram equalization for the multiple color-filter aperture (MCA) system with insufficient amount of incoming light. From the image acquired by the MCA system, we can estimate the depth information of objects at different distances by measuring the amount of misalignment among the RGB color planes. The estimated depth information is used to obtain multifocused images together with the process of the region-of-interests (ROIs) classification, registration, and fusion. However, the MCA system results in the low-exposure problem because of the limited size of the apertures. For overcoming this problem, we propose adaptive region-based histogram equalization. Based on the experimental results, the proposed algorithm is proved to be able to obtain in-focused images under the low light level environment.